Evidence-based decision-making is a useful framework for the development of policies and practices to ensure water security, ecosystem resilience, and productive societies. The term “evidence-based” is gradually yielding to the term “data-driven” as focus shifts from specified data (i.e. fit-for purpose) to data discovery (i.e. big data) as the source of evidence.
The progression from “fit-for-purpose” data to “big data” can be attributed, in part, to unwanted watershed scale outcomes such as harmful algal blooms, conflicts over water supply, and hardships caused by extreme events. In many regions of the world, the apparent trend in unwanted events is proof that there is insufficient supply of fit-for-purpose data to guide water resources policies and practices with the precision needed to enhance security, resilience, and productivity. The logical response to this deficiency would be to increase the supply of “fit-for-purpose” data (i.e. increase funding to the most trusted water monitoring agencies); however, Moore’s Law has provided a compelling alternative.
Rapid reductions in the size and cost of microprocessors are resulting in data about almost everything, almost everywhere, almost all of the time.
The world’s capacity to store information has, apparently, doubled every 40 days since the 1980’s. With such momentum, it seems only a matter of time before “big-data” has the volume, variety, velocity, and veracity to subordinate conventional water monitoring programs. This brings me to Recommendation 1.1: Enhance data collection, include citizen science, and develop Web-based analytical tools from the report “Future Water Priorities for the Nation: Directions for the U.S. Geological Survey Water Mission Area,” which I have previously discussed in the post Strategic Recommendations for Water Monitoring for the Next 25 Years.
The notion of citizen science as a source of data is compelling. There is a wealth of data waiting to be discovered, nurtured, and enhanced. New sources of data can be propagated by strategically publishing apps which people will download so that they can contribute to a greater good.
The question is, can citizen science or big-data be fit-for-purpose, where the objective is better policies and practices for watershed management?
It’s pretty clear that current trends in data volume, variety, and velocity are heading in a useful direction. But what about veracity? You may be interested in a paper by Etter et al., (2018), recently published in Hydrology and Earth System Sciences, which addresses this question from the perspective of hydrological modelling. In this paper, the authors evaluated the value of observations based on selected criteria for accuracy and sample frequency that mimic a scenario of data sourced from citizen science. In this case, the objective performance of the data was measured by goodness of fit for a simple hydrological model calibration. However, I think it is reasonable to assume that if data are informative for model calibration, then those data may also be fit-for-purpose for a wider variety of uses.
You may find the findings of this research unsurprising. Streamflow estimates from citizens are uninformative unless errors can be reduced through training or advanced filtering of the data. To make streamflow estimates useful, the error distribution of the estimates needs to be reduced by a factor of 2.
At the reduced uncertainty, citizen science-sourced data can be informative but sample frequency becomes important.
This study is narrow in scope compared the the variety of data sources that are imaginable from crowd-sourcing or the Internet of Things, but it does provide a useful benchmark against which the enthusiasm for big data can be measured. There is a present need for fit-for-purpose water data to support immediate needs for evidence-based decision making. This can only be provided by conventional monitoring techniques and methods.
Sheer volume of data is a poor substitute for data veracity.
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